Automatic Rule Extraction from Statistical Data and Fuzzy Tree Search

Generally speaking, it is necessary to extract knowledge from an expert in a given discipline and implement this knowledge into a system when constructing an expert system. However, it is not easy to extract knowledge in such fields as medical diagnosis or pattern recognition because the inference logic depends on the experience and intuition of the expert. This paper proposes an automatic rule extraction mechanism using statistical analysis. In this system, production rules are expressed in the form of the threshold function. Because the threshold function can describe any kinds of inference logic, it is expressed easily as a linear combination of input vectors and weighting coefficients. Thus weighting coefficients can be calculated by the same method as a discriminant function. If only one threshold is defined, general Boolean logic an be expressed. Moreover, an ambiguous inference rule can be expressed when the threshold levels are multidefined and a membership function is defined at each category. Further, the Fuzzy Tree Search algorithm which combines ambiguous inference and tree search is proposed at the end of this paper. This algorithm can search and determine an optimum cluster with little calculation and good performance. In practice, a medical diagnostic system applied to psychiatry which has most ambiguous diagnostic logic, has been constructed based on this algorithm and inference rules have been extracted automatically. By this experiment, Fuzzy Tree Search is as fast as the tree search technique and has as good a performance as a full search clustering technique.